One particular thorny issue is when a client receives a new model from the server. This new model must be readjusted to the client in order to recover a good personalization, while preserving its recently obtained generalization. The solution commonly used today is to fine-tune (train) the received model with client data kept for this purpose. In practice, this requires a carefully selected set of representative training data to obtain satisfactory performances [1]}. However, such setting is not realistic in mobile pervasive computing where data storage must be kept low and data characteristic (distribution) can change dramatically. To account for this variability, a solution is to use the data regularly collected by the client to progressively adapt the received model. But such naive approach exposes clients to the problem of catastrophic forgetting [2]}, which appears when a neural network is optimized on new data that are too different from the data previously used for training.
